Quasi-Experimental Design: Methods & Applications


What is a Quasi-Experimental Design? panel research
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Quasi-experimental design, a fascinating method in the realm of research, offers a unique approach to uncovering cause-and-effect relationships. Unlike traditional experiments, where researchers randomly assign participants to groups, studies work with real-world constraints, employing non-random criteria for group allocation. This flexibility makes it a practical choice for exploring complex scenarios where strict experimental controls aren’t feasible or ethical.

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What is a Quasi-Experimental Design?

What is a Quasi-Experimental Design? panel research

Think of quasi-experimental design as a clever way scientists investigate cause and effect without the strict rules of a lab. Instead of assigning subjects randomly, researchers work with what they’ve got, making it more practical for real-world situations.

In experimental research, researchers divide participants into an experimental group and a control group. The experimental group receives the treatment being studied, while the control group does not. In a true experiment, researchers determine which group each participant joins through a random assignment. 

However, some studies choose a different approach called quasi-experimental research design. This design also aims to uncover cause-and-effect relationships between variables, but it assigns participants to groups using non-random criteria.

Types of Quasi-Experimental Designs

Let’s explore the most common types of designs:

Regression Discontinuity:

  • The regression discontinuity approach measures the impact of the treatment, or independent variable, by applying a treatment assignment mechanism based on a continuous eligibility index with a continuous distribution.
  • The selection process for the treatment group involves determining whether participants’ values on a predetermined numeric rating scale exceed a designated threshold. Individuals who surpass this threshold receive the treatment, while those who fall below it do not, thereby becoming part of the control group instead.

Non-Equivalent Group Design:

  • In the non-equivalent groups design, researchers choose two groups: one receives the treatment while the other does not. This method does not entail random assignment, as researchers work with pre-existing groups and do not allocate subjects to either group randomly. Although researchers strive to select two groups as similar as possible, it remains uncertain whether the groups are genuinely comparable.
  • The design earned its name, “non-equivalent groups design,” precisely because it acknowledges the probable lack of equivalence between the two groups. This recognition stems from the understanding that creating truly comparable groups through random assignment is highly unlikely.

Natural Experiments:

  • In both laboratory and field experiments, researchers typically decide how to assign subjects to groups, whether through random or non-random criteria. However, in natural experiments, a naturally occurring external event or situation causes the random assignment of subjects to different groups. Consequently, factors influencing assignment lie beyond the investigators’ control.
  • Many natural experiments utilize a method resembling random assignment, but they still don’t qualify as true experiments due to their reliance on the observational method. Researchers commonly employ natural experiments in situations such as policy changes, weather events, and natural disasters.

These quasi-experimental designs offer researchers flexible alternatives to traditional experiments, allowing exploration of cause-and-effect relationships in various contexts.

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Example of Quasi-Experimental Designs

Through these examples, we’ll uncover the practical applications and implications of quasi-experimental research method, Here are real-Life Example 

Before-and-After Studies:

Imagine a community health initiative introducing a new fitness program to combat obesity. Researchers assess participants’ weight and fitness levels before and after the program implementation. While participants weren’t randomly assigned, the program’s impact on health outcomes can still be evaluated.

Interrupted Time Series Analysis:

Consider a study evaluating the effect of a new traffic regulation on accident rates. Accident data is collected over several months before and after the regulation’s implementation. By analyzing trends in accident frequency, researchers can assess whether the intervention had a significant impact.

Comparison with Historical Controls:

Picture a hospital implementing a new surgical procedure for a specific medical condition. Patients undergoing the new procedure are compared with similar patients who received conventional treatment in the past. Although not randomly assigned, comparing outcomes between the two groups provides valuable insights into the procedure’s effectiveness.

These examples demonstrate how quasi-experimental designs can be applied in real-world settings to assess the impact of interventions or changes, despite the absence of randomization.


Application of the Quasi-Experimental Design

Quasi-experimental studies have lower internal validity than true experiments and also cannot establish a causal relationship between variables as effectively. So why do researchers use it? 

There are certain situations where the use of a quasi-experimental design is more suited to the study. This is especially true for studies where it would be unethical to withhold treatment from a subject on a random basis. In such situations, researchers can utilize quasi-experimental design to circumvent any ethical issues.

Additionally, another context in which a quasi-experimental design is more appropriate is when the true experiment design is not feasible. This could be due to the high expenses associated with true experiments. It could also be due to the fact that true experiments generally involve a lot of work to effectively design an experimental intervention for the threshold of subjects required to justify the research as a true experiment.

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Advantages of the Quasi-Experimental Design

The following are a few advantages of utilizing a quasi-experimental research design:

  • Less Expensive: One of the most prominent advantages of quasi-experimental studies is that they are less expensive and require relatively fewer resources than randomized controlled trials. 
  • Higher External Validity: Relative to true experiments, quasi-experimental studies tend to have higher external validity as they often involve real-world interventions rather than artificial laboratory settings. This makes it more likely to reflect real-world situations and settings. 

Disadvantages of the Quasi-Experimental Design

The following are a few disadvantages of utilizing a quasi-experimental research design:

  • Risk of Confounding Bias: The absence of randomization in quasi-experimental designs complicates or even renders impossible, in some cases, the elimination of confounding variables and their impact on the causal relationship under investigation.
  • Low Internal Validity: Compared to true experiments, quasi-experimental studies have lower internal validity and therefore aren’t as effective in establishing causality. 


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Differences between quasi-experiments and true experiments

Quasi-experimental designs and true experiments are both research methods used to investigate cause-and-effect relationships. However, they differ significantly in several key aspects:


True Experiments


Control over Variables

Researchers have strict control over variables.

Researchers have less control over variables.


Random assignment of participants to groups.

Non-random assignment of participants to groups.

Ethical Considerations

Ethical concerns may arise due to the manipulation of variables or withholding treatment.

May be preferred in situations where random assignment is impractical or unethical.

Internal Validity

Generally higher due to randomization.

Generally lower due to lack of randomization.

while both quasi-experimental designs and true experiments aim to uncover cause-and-effect relationships, they differ in terms of control over variables, randomization, and ethical considerations. Understanding these differences is crucial for researchers when selecting the most appropriate research method for their study.


In conclusion, quasi-experimental designs offer valuable opportunities for researchers to investigate cause-and-effect relationships in diverse fields, ranging from healthcare to social sciences. Despite their limitations, such as the risk of confounding bias and lower internal validity compared to true experiments, quasi-experimental methods provide a practical and ethical approach to studying complex phenomena. 

By leveraging innovative designs like regression discontinuity and natural experiments, researchers can navigate the complexities of real-world scenarios while generating meaningful insights. As we continue to explore the boundaries of research methodology, platforms like Voxco provide essential tools and support for conducting and analyzing, driving advancements in knowledge and understanding.

FAQs on Quasi-Experimental Design

What is a quasi-experimental research design?

The quasi-experimental design, similar to true experiments, is a research design that aims to identify the causal relationship between an independent and dependent variable. However, unlike true experiments, quasi-experimental studies utilize non-random criteria while assigning subjects to groups.

What are the different types of quasi-experimental research designs?

Some common types of quasi-experimental designs are regression discontinuity, nonequivalent groups design, and natural experiments.

What are some of the advantages of the quasi-experimental design?

Some advantages of quasi-experimental studies are that, when compared to true experiments, they are less expensive and have higher external validity.

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